Advanced Control and Optimization for Future Grid with Energy Storage Devices

In the future grid environment, more sustainable resources will be increasing steadily. Their inherent unpredictable and intermittent characteristics will inevitably cause adverse impacts on the system static, dynamic and economic performance simultaneously. In this context, energy storage (ES) devices have been receiving growing attention because of their significant falling prices. Therefore, how to utilize these ES to help alleviate the problem of renewable energy (RE) sources integration has become more and more attractive. In my thesis, I will try to resolve some of the related problems from several perspectives. First of all, a comprehensive Future Australian transmission network simulation platform is constructed in the software DIgSILENT. Then in-depth research has been done on the aspect of frequency controller design. Based on mathematical reasoning, an advanced robust H∞ Load Frequency Controller (LFC) is developed, which can be used to assist the power system to maintain a stable frequency when accommodating more renewables. Afterwards, I develop a power system sensitivity analysis based-Enhanced Optimal Distributed Consensus Algorithm (EODCA). In the following study, a Modified Consensus Alternating Direction Method of Multipliers (MC-ADMM) is proposed, with this approach it can be verified that the convergence speed is notably accelerated even for complex large dimensional systems. Overall, in the Master thesis, I successfully provide several novel and practical solutions, algorithms and methodologies in regards to tackling both the frequency, voltage and the power flow issues in a future grid with the assistance of energy storage devices. The scientific control and optimal dispatch of these facilities could provide us with a promising approach to mitigate the potential threats that the intermittent renewables posed on the power system in the following decades

Attitudes of the Youth towards Entrepreneurs and Entrepreneurship: A Cross-cultural Comparison of India and China

This study argues that social support is an important enabler in entrepreneurial activity in a country or a region. One untested assumption in policy making on entrepreneurship development has been that all regions are equally desirous of entrepreneurial activity and one policy could address issues in all regions. It was argued that societal attitudes towards entrepreneurs and entrepreneurship are important determinants for future entrepreneurial activity. These attitudes would be impacted by the family background of an individual and entrepreneurial development in the region an individual comes from. It was hypothesized that more positive attitude would be seen in (i) people form entrepreneurial backgrounds, and (ii) entrepreneurially more developed regions. These hypotheses were tested on more than 5,000 respondents in India and China. The results for family background’s influence on attitudes found strong support in both India and China. Regional development showed stronger influence on attitude in India than in China. The findings and implications for studying attitudes and policy making are discussed.

Protecting Heritage Trees in Weifang City, Shandong Province, Northern China

Heritage trees are long-lived trees or notable trees with great historical significance, scientific value, or commemorative importance. Many heritage trees have been preserved in Weifang City, Shandong Province, Northern China. In this paper, the origin, species diversity, age structure, health, surrounding habitats, distribution, genera floristic composition, and challenges of protecting the heritage trees were investigated using literature analysis, field survey, and interview. There are 864 heritage trees in the city, composed of 49 species, 41 genera, and 25 families. The heritage trees are divided into three original types: religious trees, naturally dispersed and preserved wild trees, and trees with agricultural backgrounds or used as offerings. Particularly, Sophora japonica, Ziziphus jujube, Ginkgo biloba, Sabina chinensis, Platycladus orientalis, and Osmanthus fragrans are the six most common species. There are 208 individuals of 500 years or elder, 293 individuals of 300–499 years old, 359 individuals of 100–299 years old, and 4 individuals of notable trees. Most of them are distributed in low-urbanized areas of the 4 county-level cities and Linqu county, and few are distributed in high-urbanized areas of the 4 districts of Weifang City. There are 14, 12, and 6 genera belonging to the areal-types of Temperate, Cosmopolitan, and Tropic, respectively, similar to that of wild seed plants in Yishan Mountain. There are some challenges in protecting heritage trees, such as urbanization, habitat deterioration, natural disasters, anthropic activities, health decline, and inadequate management. Some protection measures that have been taken previously are summarized, and some measures that should be taken in the future are proposed

Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods

Design and Implementation of Reusable Components Using PowerBuilder

AbstractComponent technology is a key technology of software reuse. This paper investigates PowerBuilder based technology of software reuse, especially the technology of component design. To build a reusable component, reusable elements in the application system are firstly extracted. The reusable components are then used to form a reusable component library. When designing application system, suitable components are selected from the reusable library and then instantiated. Software system is implemented by composing the instanced components under a reusable framework. Practical results show that the use of reusable components can improve the efficiency of software development

Centralized Feature Pyramid for Object Detection

Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. However, the existing methods exorbitantly concentrate on the inter-layer feature interactions but ignore the intra-layer feature regulations, which are empirically proved beneficial. Although some methods try to learn a compact intra-layer feature representation with the help of the attention mechanism or the vision transformer, they ignore the neglected corner regions that are important for dense prediction tasks. To address this problem, in this paper, we propose a Centralized Feature Pyramid (CFP) for object detection, which is based on a globally explicit centralized feature regulation. Specifically, we first propose a spatial explicit visual center scheme, where a lightweight MLP is used to capture the globally long-range dependencies and a parallel learnable visual center mechanism is used to capture the local corner regions of the input images. Based on this, we then propose a globally centralized regulation for the commonly-used feature pyramid in a top-down fashion, where the explicit visual center information obtained from the deepest intra-layer feature is used to regulate frontal shallow features. Compared to the existing feature pyramids, CFP not only has the ability to capture the global long-range dependencies, but also efficiently obtain an all-round yet discriminative feature representation. Experimental results on the challenging MS-COCO validate that our proposed CFP can achieve the consistent performance gains on the state-of-the-art YOLOv5 and YOLOX object detection baselines.Comment: Code: https://github.com/QY1994-0919/CFPNe

Online Community Detection by Spectral CUSUM

We present an online community change detection algorithm called spectral CUSUM to detect the emergence of a community using a subspace projection procedure based on a Gaussian model setting. Theoretical analysis is provided to characterize the average run length (ARL) and expected detection delay (EDD), as well as the asymptotic optimality. Simulation and real data examples demonstrate the good performance of the proposed method